Abstract Depression is a leading cause of disability worldwide among adults. Cognitive models of depression, which have received strong empirical support, posit that individuals' characteristic ways of attending to, interpreting, and remembering stimuli in their environment may contribute to the development and maintenance of the disorder. To understand the etiology of these biases, many genetic association studies have been completed. However, findings so far (including those from our own work) have been somewhat limited. Although genetic association studies have value, we strongly believe that whole genome methods will provide new insights into the role of genetic variation in psychopathology. Aim 1 is to comprehensively measure phenotypes related to negative cognitive bias in a community sample of 1500 adults of European ancestry. We propose to use established behavioral, eye tracking, and electrophysiological tasks to comprehensively measure negatively biased attention, interpretation, and memory?central features of contemporary cognitive models of depression. Aim 2 is to quantify the aggregate contribution of genetic variation across the genome to cognitive vulnerability. We propose to use genomic-relatedness-matrix restricted maximum likelihood to estimate the aggregate genetic effect of approximately 900,000 polymorphisms that measure exomic and common genetic variation across the entire genome. This will provide an answer to the fundamental question of how much variance in these key phenotypes is due to variation in measured polymorphisms. Aim 3 is to develop biologically plausible cumulative genetic scores (CGS) to identify linkages between specific genetic variation and our phenotypes. We have created gene sets derived from a database of known and predicted protein interactions and from human brain atlases with genetic and anatomic information. Importantly, our large sample allows us to perform a confirmatory study for these gene sets. Upon study conclusion, we will make our data publically available so investigators can develop additional gene sets that putatively relate to cognitive vulnerability. In sum, the proposed study will provide much needed insight into the degree to which these theoretically motivated intermediate phenotypes for depression are associated with genetic variation. Although twin studies point to the possibility of a genetic etiology, no attempts have been made to quantify the contribution of measured genetic variation to these cognitive phenotypes. This will provide much needed guidance about how much variance in cognitive vulnerability to depression can be predicted with genome- wide based analyses and may help account for some of the missing heritability often associated with candidate polymorphism studies.